SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1461–1470 of 3304 papers

TitleStatusHype
Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)Code1
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Convolutional Autoencoders for Reduced-Order Modeling—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey—0
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified